QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: This paper explores whether variable selection enhances economic forecasting. While economists often remove noise from predictors, we show that economic forecast models are not sparse if the outcome is driven by latent factors. We also prove a compelling result that including noise in predictions yields greater benefits than excluding it. Empirically, we apply this method to four common forecasting applications including forecasting the U.S. inflation rate and obtain results that surpass many commonly used models that rely on dimension reduction or variable selection.
Yuan Liao is a Professor of Economics at Rutgers University. He works at the intersection of financial econometrics and statistics. He received his Ph.D. in Statistics from Northwestern University in 2010. Before joining Rutgers, Liao held a position as Assistant Professor of Statistics at University of Maryland (2012-2016), and worked at Princeton University as a postdoctoral associate (2010-2012).
This workshop is open to the Yale community. To receive announcements and invitations to attend, please subscribe at https://csap.yale.edu/quantitative-research-methods-workshop.
The Quantitative Research Methods Workshop series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.